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Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment ba...

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Detalles Bibliográficos
Autores principales: Li, Guo-Zheng, Yan, Shi-Xing, You, Mingyu, Sun, Sheng, Ou, Aihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371754/
https://www.ncbi.nlm.nih.gov/pubmed/22701510
http://dx.doi.org/10.1155/2012/837245
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author Li, Guo-Zheng
Yan, Shi-Xing
You, Mingyu
Sun, Sheng
Ou, Aihua
author_facet Li, Guo-Zheng
Yan, Shi-Xing
You, Mingyu
Sun, Sheng
Ou, Aihua
author_sort Li, Guo-Zheng
collection PubMed
description Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance.
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spelling pubmed-33717542012-06-13 Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion Li, Guo-Zheng Yan, Shi-Xing You, Mingyu Sun, Sheng Ou, Aihua Evid Based Complement Alternat Med Research Article Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance. Hindawi Publishing Corporation 2012 2012-06-03 /pmc/articles/PMC3371754/ /pubmed/22701510 http://dx.doi.org/10.1155/2012/837245 Text en Copyright © 2012 Guo-Zheng Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Guo-Zheng
Yan, Shi-Xing
You, Mingyu
Sun, Sheng
Ou, Aihua
Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title_full Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title_fullStr Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title_full_unstemmed Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title_short Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion
title_sort intelligent zheng classification of hypertension depending on ml-knn and information fusion
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371754/
https://www.ncbi.nlm.nih.gov/pubmed/22701510
http://dx.doi.org/10.1155/2012/837245
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